{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Set current directory and work relative to it\n",
    "os.chdir('D:/Practical Time Series')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nLet us shown an example of resampling to a given frequency\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Let us shown an example of resampling to a given frequency\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Read dataset into pandas DataFrame\n",
    "df = pd.read_csv('datasets/chemical-concentration-readings.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of the dataset: (197, 2)\n"
     ]
    }
   ],
   "source": [
    "#Let's see the shape of the dataset\n",
    "print('Shape of the dataset:', df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Chemical conc.</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1975-01-01 00:00:00</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1975-01-01 02:00:00</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1975-01-01 04:00:00</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1975-01-01 06:00:00</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1975-01-01 08:00:00</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1975-01-01 10:00:00</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1975-01-01 12:00:00</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1975-01-01 14:00:00</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1975-01-01 16:00:00</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1975-01-01 18:00:00</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Timestamp  Chemical conc.\n",
       "0  1975-01-01 00:00:00            17.0\n",
       "1  1975-01-01 02:00:00            16.6\n",
       "2  1975-01-01 04:00:00            16.3\n",
       "3  1975-01-01 06:00:00            16.1\n",
       "4  1975-01-01 08:00:00            17.1\n",
       "5  1975-01-01 10:00:00            16.9\n",
       "6  1975-01-01 12:00:00            16.8\n",
       "7  1975-01-01 14:00:00            17.4\n",
       "8  1975-01-01 16:00:00            17.1\n",
       "9  1975-01-01 18:00:00            17.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see first 10 rows of the DataFrame\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#The observations seem to be taken at an interval of 2 hours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Chemical conc.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Timestamp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1975-01-01 00:00:00</th>\n",
       "      <td>1975-01-01 00:00:00</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01 02:00:00</th>\n",
       "      <td>1975-01-01 02:00:00</td>\n",
       "      <td>16.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01 04:00:00</th>\n",
       "      <td>1975-01-01 04:00:00</td>\n",
       "      <td>16.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01 06:00:00</th>\n",
       "      <td>1975-01-01 06:00:00</td>\n",
       "      <td>16.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01 08:00:00</th>\n",
       "      <td>1975-01-01 08:00:00</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01 10:00:00</th>\n",
       "      <td>1975-01-01 10:00:00</td>\n",
       "      <td>16.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01 12:00:00</th>\n",
       "      <td>1975-01-01 12:00:00</td>\n",
       "      <td>16.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01 14:00:00</th>\n",
       "      <td>1975-01-01 14:00:00</td>\n",
       "      <td>17.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01 16:00:00</th>\n",
       "      <td>1975-01-01 16:00:00</td>\n",
       "      <td>17.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1975-01-01 18:00:00</th>\n",
       "      <td>1975-01-01 18:00:00</td>\n",
       "      <td>17.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                               Timestamp  Chemical conc.\n",
       "Timestamp                                               \n",
       "1975-01-01 00:00:00  1975-01-01 00:00:00            17.0\n",
       "1975-01-01 02:00:00  1975-01-01 02:00:00            16.6\n",
       "1975-01-01 04:00:00  1975-01-01 04:00:00            16.3\n",
       "1975-01-01 06:00:00  1975-01-01 06:00:00            16.1\n",
       "1975-01-01 08:00:00  1975-01-01 08:00:00            17.1\n",
       "1975-01-01 10:00:00  1975-01-01 10:00:00            16.9\n",
       "1975-01-01 12:00:00  1975-01-01 12:00:00            16.8\n",
       "1975-01-01 14:00:00  1975-01-01 14:00:00            17.4\n",
       "1975-01-01 16:00:00  1975-01-01 16:00:00            17.1\n",
       "1975-01-01 18:00:00  1975-01-01 18:00:00            17.0"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Parse the timestamp to datetime row index of the DataFrame\n",
    "datetime_rowid = df['Timestamp'].map(lambda t: pd.to_datetime(t, format='%Y-%m-%d %H:%M:%S'))\n",
    "df.index = datetime_rowid\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Resample and compute daily mean\n",
    "daily = df['Chemical conc.'].resample('D')\n",
    "daily_mean = daily.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AS3KTTCpE5F7gQGCoiCwGLgXOBq4XkRKgGTgntu1I4Hal1JFKqXYR+T7wLFAM3KGU+iBf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\nUaMSxwE9EiTTpmkravPNU/bxeuD19VA7eDyMH58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rl8GAbLopfPe7NkNhyJhwh4j936TV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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0df8ea630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot original time series and daily mean\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "df['Chemical conc.'].plot(ax=ax, color='b')\n",
    "daily_mean.plot(ax=ax, color='r')\n",
    "\n",
    "ax.set_title('Bi-hourly reading (blue) & Daily Mean (red)')\n",
    "ax.set_xlabel('Days in Jan 1975')\n",
    "ax.set_ylabel('Chemical concentration')\n",
    "\n",
    "plt.savefig('plots/ch2/B07887_02_02.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nLet us shown an example of grouping by a period\\n'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "Let us shown an example of grouping by a period\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Load the DataFrame and re-index the row to datetime64\n",
    "df = pd.read_csv('datasets/mean-daily-temperature-fisher-river.csv')\n",
    "df.index = df['Date'].map(lambda d: pd.to_datetime(d, format = '%Y-%m-%d'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of dataframe: (1461, 2)\n"
     ]
    }
   ],
   "source": [
    "#Display shape of the DataFrame\n",
    "print('Shape of dataframe:', df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean temparature</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-02</th>\n",
       "      <td>1988-01-02</td>\n",
       "      <td>-20.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-03</th>\n",
       "      <td>1988-01-03</td>\n",
       "      <td>-22.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-04</th>\n",
       "      <td>1988-01-04</td>\n",
       "      <td>-30.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-05</th>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>-31.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-06</th>\n",
       "      <td>1988-01-06</td>\n",
       "      <td>-27.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-07</th>\n",
       "      <td>1988-01-07</td>\n",
       "      <td>-26.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-08</th>\n",
       "      <td>1988-01-08</td>\n",
       "      <td>-26.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-09</th>\n",
       "      <td>1988-01-09</td>\n",
       "      <td>-23.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-10</th>\n",
       "      <td>1988-01-10</td>\n",
       "      <td>-23.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Mean temparature\n",
       "Date                                    \n",
       "1988-01-01  1988-01-01            -23.00\n",
       "1988-01-02  1988-01-02            -20.50\n",
       "1988-01-03  1988-01-03            -22.00\n",
       "1988-01-04  1988-01-04            -30.50\n",
       "1988-01-05  1988-01-05            -31.00\n",
       "1988-01-06  1988-01-06            -27.50\n",
       "1988-01-07  1988-01-07            -26.25\n",
       "1988-01-08  1988-01-08            -26.50\n",
       "1988-01-09  1988-01-09            -23.00\n",
       "1988-01-10  1988-01-10            -23.50"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's see first 10 rows\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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AZuXSnXc27pxJocRFb6vrEuDTTpMVkIccUt85W1vt8XCzLsBBlXCNTOOYMTK/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\n5J8p0aoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0de79ed68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot original time series on daily mean temparature\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "df['Mean temparature'].plot(ax=ax, color='b')\n",
    "\n",
    "ax.set_title('Mean daily temparature')\n",
    "\n",
    "plt.savefig('plots/ch2/B07887_02_03.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean temparature</th>\n",
       "      <th>Month_Year</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1988-01-01</th>\n",
       "      <td>1988-01-01</td>\n",
       "      <td>-23.00</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-02</th>\n",
       "      <td>1988-01-02</td>\n",
       "      <td>-20.50</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-03</th>\n",
       "      <td>1988-01-03</td>\n",
       "      <td>-22.00</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-04</th>\n",
       "      <td>1988-01-04</td>\n",
       "      <td>-30.50</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-05</th>\n",
       "      <td>1988-01-05</td>\n",
       "      <td>-31.00</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-06</th>\n",
       "      <td>1988-01-06</td>\n",
       "      <td>-27.50</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-07</th>\n",
       "      <td>1988-01-07</td>\n",
       "      <td>-26.25</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-08</th>\n",
       "      <td>1988-01-08</td>\n",
       "      <td>-26.50</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-09</th>\n",
       "      <td>1988-01-09</td>\n",
       "      <td>-23.00</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1988-01-10</th>\n",
       "      <td>1988-01-10</td>\n",
       "      <td>-23.50</td>\n",
       "      <td>01-1988</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Date  Mean temparature Month_Year\n",
       "Date                                               \n",
       "1988-01-01  1988-01-01            -23.00    01-1988\n",
       "1988-01-02  1988-01-02            -20.50    01-1988\n",
       "1988-01-03  1988-01-03            -22.00    01-1988\n",
       "1988-01-04  1988-01-04            -30.50    01-1988\n",
       "1988-01-05  1988-01-05            -31.00    01-1988\n",
       "1988-01-06  1988-01-06            -27.50    01-1988\n",
       "1988-01-07  1988-01-07            -26.25    01-1988\n",
       "1988-01-08  1988-01-08            -26.50    01-1988\n",
       "1988-01-09  1988-01-09            -23.00    01-1988\n",
       "1988-01-10  1988-01-10            -23.50    01-1988"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#We need to groupby the data for every month and find aggregate statistics\n",
    "\n",
    "#Let's start by adding a Month_Year column\n",
    "df['Month_Year'] = df.index.map(lambda d: d.strftime('%m-%Y'))\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month_Year</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-1988</td>\n",
       "      <td>-22.137097</td>\n",
       "      <td>-23.00</td>\n",
       "      <td>5.260640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01-1989</td>\n",
       "      <td>-17.129032</td>\n",
       "      <td>-18.00</td>\n",
       "      <td>8.250725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01-1990</td>\n",
       "      <td>-15.112903</td>\n",
       "      <td>-12.00</td>\n",
       "      <td>6.606764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>01-1991</td>\n",
       "      <td>-23.038710</td>\n",
       "      <td>-24.50</td>\n",
       "      <td>7.095570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02-1988</td>\n",
       "      <td>-19.025862</td>\n",
       "      <td>-19.50</td>\n",
       "      <td>8.598522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>02-1989</td>\n",
       "      <td>-19.267857</td>\n",
       "      <td>-19.25</td>\n",
       "      <td>8.092042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>02-1990</td>\n",
       "      <td>-17.482143</td>\n",
       "      <td>-16.50</td>\n",
       "      <td>8.018477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>02-1991</td>\n",
       "      <td>-10.967857</td>\n",
       "      <td>-12.15</td>\n",
       "      <td>8.220753</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>03-1988</td>\n",
       "      <td>-8.258065</td>\n",
       "      <td>-9.25</td>\n",
       "      <td>5.341459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>03-1989</td>\n",
       "      <td>-12.508065</td>\n",
       "      <td>-9.50</td>\n",
       "      <td>8.289925</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Month_Year       mean  median       std\n",
       "0    01-1988 -22.137097  -23.00  5.260640\n",
       "1    01-1989 -17.129032  -18.00  8.250725\n",
       "2    01-1990 -15.112903  -12.00  6.606764\n",
       "3    01-1991 -23.038710  -24.50  7.095570\n",
       "4    02-1988 -19.025862  -19.50  8.598522\n",
       "5    02-1989 -19.267857  -19.25  8.092042\n",
       "6    02-1990 -17.482143  -16.50  8.018477\n",
       "7    02-1991 -10.967857  -12.15  8.220753\n",
       "8    03-1988  -8.258065   -9.25  5.341459\n",
       "9    03-1989 -12.508065   -9.50  8.289925"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Calculate month wise statistics\n",
    "monthly_stats = df.groupby(by='Month_Year')['Mean temparature'].aggregate([np.mean, np.median,\n",
    "                                                                           np.std\n",
    "                                                                          ])\n",
    "monthly_stats.reset_index(inplace=True)\n",
    "monthly_stats.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month_Year</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>std</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01-1988</td>\n",
       "      <td>-22.137097</td>\n",
       "      <td>-23.000</td>\n",
       "      <td>5.260640</td>\n",
       "      <td>1988</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02-1988</td>\n",
       "      <td>-19.025862</td>\n",
       "      <td>-19.500</td>\n",
       "      <td>8.598522</td>\n",
       "      <td>1988</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>03-1988</td>\n",
       "      <td>-8.258065</td>\n",
       "      <td>-9.250</td>\n",
       "      <td>5.341459</td>\n",
       "      <td>1988</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>04-1988</td>\n",
       "      <td>2.641667</td>\n",
       "      <td>1.875</td>\n",
       "      <td>5.057720</td>\n",
       "      <td>1988</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>05-1988</td>\n",
       "      <td>11.290323</td>\n",
       "      <td>11.000</td>\n",
       "      <td>6.254364</td>\n",
       "      <td>1988</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>06-1988</td>\n",
       "      <td>19.291667</td>\n",
       "      <td>19.000</td>\n",
       "      <td>3.909032</td>\n",
       "      <td>1988</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>07-1988</td>\n",
       "      <td>19.048387</td>\n",
       "      <td>18.500</td>\n",
       "      <td>3.073692</td>\n",
       "      <td>1988</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>08-1988</td>\n",
       "      <td>17.379032</td>\n",
       "      <td>18.000</td>\n",
       "      <td>3.183205</td>\n",
       "      <td>1988</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>09-1988</td>\n",
       "      <td>10.675000</td>\n",
       "      <td>10.750</td>\n",
       "      <td>3.880294</td>\n",
       "      <td>1988</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10-1988</td>\n",
       "      <td>2.467742</td>\n",
       "      <td>3.000</td>\n",
       "      <td>6.697245</td>\n",
       "      <td>1988</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Month_Year       mean  median       std  Year Month\n",
       "0     01-1988 -22.137097 -23.000  5.260640  1988    01\n",
       "4     02-1988 -19.025862 -19.500  8.598522  1988    02\n",
       "8     03-1988  -8.258065  -9.250  5.341459  1988    03\n",
       "12    04-1988   2.641667   1.875  5.057720  1988    04\n",
       "16    05-1988  11.290323  11.000  6.254364  1988    05\n",
       "20    06-1988  19.291667  19.000  3.909032  1988    06\n",
       "24    07-1988  19.048387  18.500  3.073692  1988    07\n",
       "28    08-1988  17.379032  18.000  3.183205  1988    08\n",
       "32    09-1988  10.675000  10.750  3.880294  1988    09\n",
       "36    10-1988   2.467742   3.000  6.697245  1988    10"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's create month and year columns and sort by them to reorder the rows\n",
    "monthly_stats['Year'] = monthly_stats['Month_Year']\\\n",
    "                        .map(lambda m: pd.to_datetime(m, format='%m-%Y').strftime('%Y'))\n",
    "monthly_stats['Month'] = monthly_stats['Month_Year']\\\n",
    "                        .map(lambda m: pd.to_datetime(m, format='%m-%Y').strftime('%m'))\n",
    "monthly_stats.sort_values(by=['Year', 'Month'], inplace=True)\n",
    "monthly_stats.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Let's set the Month_Year as the row index\n",
    "monthly_stats.index = monthly_stats['Month_Year']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h5mHMPCzPzhJRCVAWVa9ejh5mN4WFEtqyaZM7x3OKykrxYbppAQPhE+AuXaSW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0df1ea048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot original time series and daily mean\n",
    "fig = plt.figure(figsize=(5.5, 5.5))\n",
    "ax = fig.add_subplot(1,1,1)\n",
    "\n",
    "monthly_stats['mean'].plot(ax=ax, color='b')\n",
    "monthly_stats['std'].plot(ax=ax, color='r')\n",
    "\n",
    "ax.set_title('Monthly statistics: Mean (blue) & Std. Dev. (red)')\n",
    "\n",
    "plt.savefig('plots/ch2/B07887_02_04.png', format='png', dpi=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Now we will calculate weekly moving average on the original time series of mean daily temparature\n",
    "weekly_moving_average = df['Mean temparature'].rolling(7).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Now we will calculate monthly moving average on the original time series of mean daily temparature\n",
    "monthly_moving_average = df['Mean temparature'].rolling(30).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Let's caluclate the weekly and monthly avergaes with a stride of length 2\n",
    "weekly_moving_average_2stride = df['Mean temparature'].rolling(7).mean()[::2]\n",
    "monthly_moving_average_2stride = df['Mean temparature'].rolling(30).mean()[::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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558mG3haoTCDRAo4ffHD4bBu2BFxRIeqNE04weqnffjOeg0uXmo+rXz9jieHH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\nYnLVJO12GqtPrKZvw76KPSncS7BXMDvP7iQ+Ld4o8k1BSGIIu87u4l9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VfXlnxztGWYaI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\nwvQtV1zhNpUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1d0dfbbcc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot original time series and weekly moving average\n",
    "fig, axarr = plt.subplots(3, sharex=True)\n",
    "fig.set_size_inches(5.5, 5,5)\n",
    "\n",
    "df['Mean temparature'].plot(ax=axarr[0], color='b')\n",
    "axarr[0].set_title('Daily mean temparature')\n",
    "\n",
    "weekly_moving_average.plot(ax=axarr[1], color='r')\n",
    "axarr[1].set_title('Weekly moving average')\n",
    "\n",
    "monthly_moving_average.plot(ax=axarr[2], color='g')\n",
    "axarr[2].set_title('Monthly moving average')\n",
    "\n",
    "plt.savefig('plots/ch2/B07887_02_05.png', format='png', dpi=300)"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
